Models of the diffuse radar backscatter from Mars

The topographies of several debris flow units near the Mount St. Helens Volcano were measured at lateral scales of millimeters to meters in September 1990. The objective was to measure the surface roughness of the debris flows at scales smaller than, on the order of, and larger that the radar wavelength of common remote sensing radars. A laser profiling system and surveying instruments were used to obtain elevation data for square areas that varied in size from 10 to 32 cm. The elevation data were converted to estimates of the power spectrum of surface roughness. The conversions were based upon standard periodogram techniques, and upon a modified spectral estimation technique that was developed

Cognitively Engineering a Virtual Collaboration Environment for Crisis Response

Crisis response situations require collaboration across many different organizations with different backgrounds, training, procedures, and goals. The Indian Ocean Tsunami in 2004 and the Hurricane Katrina relief efforts in 2005 emphasized the importance of effective communication and collaboration. In the former, the Multinational Planning Augmentation Team (MPAT) supported brokering of requests for assistance with offers of help from rapidly deployed military and humanitarian assistance facilities. In the aftermath of Hurricane Katrina, the National Guard Soldiers and active component Army Soldiers assisted other state, federal, and non-government organizations with varying degrees of efficiency and expediency. Compounding the challenges associated with collaboration during crisis situations is the distributed nature of the supporting organizations and the lack of a designated leader across these military, government, nongovernment organizations. The Army Research Laboratory is collaborating with the University of Edinburgh, University o

Graph-based Semi-Supervised & Active Learning for Edge Flows

We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to predict the flows on the remaining edges. To this end, we develop a computational framework that imposes certain constraints on the overall flows, such as (approximate) flow conservation. These constraints render our approach different from classical graph-based SSL for vertex labels, which posits that tightly connected nodes share similar labels and leverages the graph structure accordingly to extrapolate from a few vertex labels to the unlabeled vertices. We derive bounds for our method's reconstruction error and demonstrate its strong performance on synthetic and real-world flow networks from transportation, physical infrastructure, and the Web. Furthermore, we provide two active learning algorithms for selecting informative edges on which to measure flow, which has applications for optimal sensor deployment. The first strategy selects edges to minimize the reconstruction error bound and works well on flows that are approximately divergence-free. The second approach clusters the graph and selects bottleneck edges that cross cluster-boundaries, which works well on flows with global trends

Diffuse optical tomography to investigate the newborn brain

Over the past 15 years, functional near-infrared spectroscopy (fNIRS) has emerged as a powerful technology for studying the developing brain. Diffuse optical tomography (DOT) is an extension of fNIRS that combines hemodynamic information from dense optical sensor arrays over a wide field of view. Using image reconstruction techniques, DOT can provide images of the hemodynamic correlates to neural function that are comparable to those produced by functional magnetic resonance imaging. This review article explains the principles of DOT, and highlights the growing literature on the use of DOT in the study of healthy development of the infant brain, and the study of novel pathophysiology in infants with brain injury. Current challenges, particularly around instrumentation and image reconstruction, will be discussed, as will the future of this growing field, with particular focus on whole-brain, time-resolved DOT

Self-referenced characterization of space-time couplings in near single-cycle laser pulses

We report on the characterization of space-time couplings in high energy sub-2-cycle 770nm laser pulses using a self-referencing single-shot method. Using spatially-encoded arrangement filter-based spectral phase interferometry for direct electric field reconstruction (SEA-F-SPIDER) we characterize few-cycle pulses with a wave-front rotation of 2.8x?10^11 rev/sec (1.38 mrad per half-cycle) and pulses with pulse front tilts ranging from to -0.33 fs/um to -3.03 fs/um.Comment: 6 pages, 6 figure

The space shuttle launch vehicle aerodynamic verification challenges

The Space Shuttle aerodynamics and performance communities were challenged to verify the Space Shuttle vehicle (SSV) aerodynamics and system performance by flight measurements. Historically, launch vehicle flight test programs which faced these same challenges were unmanned instrumented flights of simple aerodynamically shaped vehicles. However, the manned SSV flight test program made these challenges more complex because of the unique aerodynamic configuration powered by the first man-rated solid rocket boosters (SRB). The analyses of flight data did not verify the aerodynamics or performance preflight predictions of the first flight of the Space Transportation System (STS-1). However, these analyses have defined the SSV aerodynamics and verified system performance. The aerodynamics community also was challenged to understand the discrepancy between the wind tunnel and flight defined aerodynamics. The preflight analysis challenges, the aerodynamic extraction challenges, and the postflight analyses challenges which led to the SSV system performance verification and which will lead to the verification of the operational ascent aerodynamics data base are presented